Context engineering is a crucial discipline for developing reliable large language model (LLM) systems, as it involves designing and optimizing the surrounding information that influences model outputs. It addresses the nearly-infinite data potential in LLM requests, optimizing the use of context windows to ensure models process relevant and accurate information efficiently. Essential components include system prompts, user input, conversation history, retrieved knowledge, tool descriptions, task metadata, examples, and hidden content. Effective context engineering requires principles like relevance, provenance, compression, hierarchical control, adaptivity, observability, efficiency, and safety. It involves nuanced decisions on managing retrieved knowledge, conversation history, and tool interactions, as well as using few-shot examples to enhance model performance. Governance and evaluation are integral to maintaining reliable, auditable, and compliant systems, using practices such as version control, access management, policy enforcement, and rigorous testing. Platforms like Opik aid in capturing and analyzing LLM interactions, providing visibility into context usage and enabling continuous improvement in context engineering.